Convolutional Neural Networks (CNN) have powerful pattern-recognition
capabilities that have recently given dramatic improvements in important tasks
such as image classification [1]}.
However, as CNN are increasingly being applied in real-world, safety-critical domains, their vulnerability to adversarial examples [2]}, [3]}, and their poor uncertainty estimates are becoming increasingly problematic.
Bayesian inference is a theoretically principled and demonstrably successful [4]}, [5]} framework for learning in the face of uncertainty, which may also help to address the problems of adversarial examples [6]}.
Unfortunately, Bayesian inference in CNN is extremely difficult due to the very large number of parameters, requiring highly approximate factorised variational approximations [7]}, [8]}, or requiring the storage [9]} of large numbers of posterior samples [10]}, [11]}.
